Generative AI Engineer — New York, NY

About the Role

The Senior AI Engineer reports to the Lead AI Engineer and serves as a key technical partner in designing and delivering secure, scalable AI solutions across the firm. This is a hands-on engineering role responsible for the technical design and implementation of assigned AI initiatives, while contributing to shared engineering standards.

You will work closely with the Lead AI Engineer, Data, Platform Engineering, Cybersecurity, and product teams to move AI products reliably into production. The role combines agentic AI development, enterprise integrations, evaluation, security, and production engineering, with opportunities to mentor other AI engineers and influence technical decisions across initiatives.

Key Responsibilities

• Partner with the Lead AI Engineer to develop and apply reference architectures, engineering patterns, and standards for generative AI, machine learning, retrieval, and agentic workflows.

• Own the technical design and implementation of assigned AI products, including model selection, orchestration, context management, retrieval, tools, memory, APIs, and integrations within the established architecture.

• Build evaluation frameworks to measure quality, groundedness, safety, latency, reliability, and cost, including regression testing and production release checks.

• Implement security and governance controls covering identity, access, data handling, privacy, secrets, logging, auditability, human oversight, and safe failure in collaboration with Cybersecurity and the Lead AI Engineer.

• Partner with Data and Platform Engineering to develop reusable services and support deployment, CI/CD, MLOps or LLMOps, observability, scaling, and production operations.

• Evaluate models, platforms, vendors, and emerging technologies; develop prototypes and provide recommendations for architecture and build-versus-buy decisions.

• Develop critical components, review code and designs, troubleshoot complex technical issues, and improve performance, maintainability, and reliability.

• Document solution designs, architecture decisions, production-readiness checks, and operational handoffs according to established standards.

• Mentor AI engineers and provide day-to-day technical guidance through design reviews, reusable components, and shared learning.

• Communicate technical trade-offs and delivery risks, serve as a technical resource for the team, and support the Lead AI Engineer in coordinating technical work across initiatives.

Required Qualifications

• Bachelor’s or advanced degree in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.

• 6–10+ years of experience in software, data, ML, or AI engineering, including the technical design and delivery of production systems.

• Strong hands-on experience with LLM APIs and models, RAG, embeddings, vector search, agents, orchestration, evaluation, and AI application security.

• Production experience building and operating agentic systems, including agent harnesses and SDKs such as Claude Code, Claude Agent SDK, OpenAI Agents SDK, LangChain, or equivalent; multi-step tool-using agents; subagent orchestration; and context and memory management across long-running tasks.

• Experience designing and deploying Model Context Protocol (MCP) servers and tools, including tool schema design, authentication and authorization, read-only versus write-scoped tools, and secure access to enterprise data and systems.

• Experience designing human-in-the-loop workflows, including approval gates, structured input collection, asynchronous job orchestration (start, poll status, and resume), and auditable handoffs between agents and business users.

• Experience building evaluation and regression harnesses for agent outputs, including deterministic checks of numerical and structured results against golden baselines, alongside LLM-based quality, groundedness, and safety evaluations.

• Strong Python and software engineering skills, including APIs, distributed systems, cloud services, testing, CI/CD, observability, and secure development.

• Experience integrating AI solutions with enterprise data platforms, identity systems, document stores, workflows, and business applications.

• Ability to balance rapid experimentation with reliability, governance, maintainability, and cost.

• Ability to independently own complex technical work, collaborate across teams, communicate design decisions, and mentor engineers.

Preferred Qualifications

• Experience with Snowflake, Azure or AWS, modern data stacks, containers, infrastructure as code, and enterprise AI platforms.

• Experience in financial services, real estate, private equity, asset management, or another regulated environment.